Graph-based approaches leverage the topological information of the input to perform local operations in the respective neighborhood only and can learn efficiently on unstructured data. Recently GDL methods have shown promising performance for their applications in the mechanics field as well [1]}, [2]}, [3]}, [4]}, [5]}. More recently [6]} proposed MAgNET, a novel graph U-Net framework for efficiently learning on mesh-based data. In this work we utilise it to accurately predict non-linear deformations of solids.
